Solving TSP Problems with Simulated Annealing Algorithm: MATLAB Implementation

I would like to share a MATLAB implementation that applies simulated annealing algorithm to solve the Traveling Salesman Problem (TSP). This code serves as a practical resource for better understanding and implementing simulated annealing in combinatorial optimization scenarios. The implementation incorporates several optimization strategies including temperature scheduling, neighborhood search mechanisms, and acceptance probability calculations using the Metropolis criterion. Key algorithmic components feature energy function computation based on route distance, perturbation methods for generating new solutions through city swaps or reversals, and cooling schedule management with geometric temperature reduction. The code structure includes modular functions for solution initialization, cost evaluation, and convergence monitoring, allowing for clear observation of the algorithm's progression toward optimal solutions. Through collaborative discussion and analysis of this implementation, we can exchange insights on parameter tuning, convergence behavior, and performance optimization techniques. I welcome everyone to join the discussion and explore together the applications and refinement methods of simulated annealing in solving TSP and related optimization challenges.